Abstract

Processes exhibit complex behavior in chemical industries which makes the development of reliable theoretical models a very difficult and time consuming task. The resulting models are also often complex which poses additional problem for robust on-line process fault detection, diagnosis and control of these processes. Efficient process fault detection and diagnosis in processes is important to reduce the cost of producing products with undesired specifications. Multivariate Statistical Process Control (MSPC) uses historical data of processes to develop useful process fault detection, diagnosis and control tools. Thus, the availability of theoretical models is not an important factor in the implementation of MSPC on processes. The present fault detection and diagnosis (FDD) method based on MSPC uses statistical control charts and contribution plots. These charts are efficient in fault detection but ambiguous in diagnosis of fault cause of detected faults due to the absence of control limits in the contribution plots. In this research work, an FDD algorithm is developed using MSPC and correlation coefficients between process variables. Normal Correlation (NC), Modified Principal Component Analysis (PCA) and
Partial Correlation Analysis (PCorrA) are used to develop the correlation coefficients between selected key process variables and quality variables of interest. Shewhart Control Chart (SCC) and Range Control Chart (RCC) are used with the developed correlation coefficients for FDD. The developed FDD algorithm was implemented on a simulated distillation column which is a single equipment process. Results showed that the developed FDD algorithm successfully detect and diagnosed the pre-designed faults. The implementation of the developed FDD algorithm on a chemical plant can reduce the operational cost due to early detection and diagnosis of faults in the process and improving the performance of the plant.